Identifying EEG Parameters to Monitor Stroke Rehabilitation using Individual Analysis

Setiawan, Hendra; Islamiyah, Wardah Rahmatul; Wibawa, Adhi Dharma; Purnomo, Mauridhi Hery

doi:10.1109/isitia.2019.8937238

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…Stroke can occur either by an interruption of the blood flow (ischemic stroke) or by a blood vessel rupture (hemorrhagic stroke) and cause loss of muscle strength on one side of the body. Hemiplegia is a paralysis of one side of the body, whereas hemiparesis is a partial loss of movement of one side of the body (Setiawan et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

G-Exos: A wearable gait exoskeleton for walk assistance

Zorkot¹,

Dac²,

Morya³

et al. 2022

Front. Neurorobot.

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Stroke is the second leading cause of death and one of the leading causes of disability in the world. According to the World Health Organization, 11 million people suffer a stroke yearly. The cost of the disease is exorbitant, and the most widely used treatment is conventional physiotherapy. Therefore, assistive technology emerges to optimize rehabilitation and functional capabilities, but cost, robustness, usability, and long-term results still restrict the technology selection. This work aimed to develop a low-cost ankle orthosis, the G-Exos, a wearable exoskeleton to increase motor capability by assisting dorsiflexion, plantarflexion, and ankle stability. A hybrid system provided near-natural gait movements using active, motor, and passive assistance, elastic band. The system was validated with 10 volunteers with foot drop: seven with stroke, two with incomplete spinal cord injury (SCI), and one with acute inflammatory transverse myelitis (ATM). The G-Exos showed assistive functionality for gait movement. A Friedman test showed a significant difference in dorsiflexion amplitude with the use of the G-Exos compared to gait without the use of the G-Exos [x2(3) = 98.56, p < 0.001]. In addition, there was also a significant difference in ankle eversion and inversion comparing walking with and without the G-Exos [x2(3) = 36.12, p < 0.001]. The G-Exos is a robust, lightweight, and flexible assistive technology device to detect the gait phase accurately and provide better human-machine interaction. G-Exos training improved capability to deal with gait disorders, usability, and motor and functional recovery. Wearable assistive technologies lead to a better quality of life and contribute using in activities of daily living.

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Section: Introductionmentioning

confidence: 99%

G-Exos: A wearable gait exoskeleton for walk assistance

Zorkot¹,

Dac²,

Morya³

et al. 2022

Front. Neurorobot.

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“…They are Delta (δ), Theta (θ), Alpha (α), Beta (β), and Gamma (γ). This experiment focuses on EEG subbands such as Alpha Low, Alpha High, Beta Low, and Beta High [28]. The UltraCortex Mark IV device (one of the OpenBCI products) was chosen for EEG data recording because it is easier to use and more portable.…”

Section: A Eeg Recordingmentioning

confidence: 99%

“…In the frequency domain, PSD (Power Spectral Density) is one of the features of the EEG signal for further analysis [28], [33], [36]. PSD represents the power distribution at a certain frequency or frequency range [37].…”

Section: Welch's Psd Feature Extractionmentioning

confidence: 99%

“…In time-domain analysis, we found that statistical parameters such as Standard Deviation (STD) and Mean Absolute Value (MAV) showed a high potential to be used as a parameter to describe the stroke condition. Another study by Setiawan et al [28] discovered that PSD (Power Spectral Density) was the best feature for describing the difference between the healthy and affected hands in stroke patients when using frequency domain analysis. However, the study only compared EEG data from the healthy and affected hands of the same stroke patient.…”

Section: Introductionmentioning

confidence: 99%

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EEG Time-Frequency Domain Analysis for Describing Healthy Subjects and Stroke Patients during Stroke Rehabilitation Motion Tasks

Wibawa¹,

Pamungkas²,

Pratiwi³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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To overcome the long-term impact of stroke attacks on society, stroke rehabilitation is the only solution WHO and many healthcare organizations suggested. Until recently, stroke rehabilitation monitoring has been done using visual observation, which has several drawbacks. EEG is a new approach to understanding how the central nervous system controls motion. This study compares the motion pattern done by a group of 12 healthy subjects and nine stroke patients during the rehabilitation motion tasks using the OpenBCI system. Time-frequency domain features, namely PSD, MAV, and STD are used to explore how the patterns differ. Three rehabilitation motions are implemented: grasping, elbow flexion extension, and shoulder flexion-extension. The result shows that the healthy cross-brain correlation happens in healthy subjects. This means that when the left-side arm does the motion, the EEG feature values from the right hemisphere are higher, and vice versa. However, this healthy cross-brain correlation pattern did not happen within the stroke patient group. The overall value of PSD, MAV, and STD from both hemispheres during all motions is higher in the healthy group than in stroke patients. The type of motion also contributes to describing the time-frequency domain feature comparison. In conclusion, this gap value using time-frequency domain features can be used as a target for stroke rehabilitation programs by implementing the EEG technology to monitor it.

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“…Pemilihan partisipan sangatlah penting dan dapat dipertanggungjawabkan jika memilih partisipan yang tepat dengan menggunakan dasar bahwa penelitian yang melibatkan manusia harus menggunakan uji kelayakan etik (ethical clearance) untuk menjaga agar proses penelitian mengikuti prinsip umum etika penelitian tentang kesehatan. Untuk itu data patisipan yang didapat bekerasama dengan Rumah Sakit Umum Universitas Airlangga (RSUA) dalam pengambilan data agar mendapat kelayakan etik dari Komite Etik RSUA [6].…”

Section: Pemilihan Partisipanunclassified

Pengambilan Data Pasien Stroke Menggunakan Sinyal EEG Sebagai Informasi Pengambilan Keputusan Melakukan Tindak Lanjut Rehabilitasi

Sulistyono

2021

J Inform Syst

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Stoke merupakan Penyakit Tidak Menular (PTM) yang merupakan jenis penyakit kardiovaskuler yang disebabkan oleh gangguan pembuluh darah dengan urutan ke dua penyebab kematian di dunia sebanyak 45% penduduk dunia meninggal akhibat stroke setiap tahunnya, bahkan di Indonesia sekitar 7% penduduk setiap tahunnya terkena stroke. Stroke dapat juga menyebabkan cacat permanen, depresi, demensia dan stress jika tidak ditangani dengan benar, karena ketidaktahuan orang mengenai stroke dan penanganannya. Dalam penanganan terhadap penyakit stroke harus mengetahui tanda-tanda yang perlu diketahui sebelum bisa memutuskan bahwa gejala tersebut merupakan penyakit stroke atau bukan. Selain melihat tanda-tanda juga perlu memeriksa tensi dan dan selalu memriksakan kondisi tubuh dengan check up rutin agar penyakit stroke dapat diminimalisir.Yang menjadi masalah dalam penanganan stroke adalah pada saat pasca terkena stroke akan melakukan rehabilitasi medis untuk mengembalikan fungsi motorik pasien. Dalam prakteknya tenaga medis sulit melakukan penilaian pengukuran yang akurat terhadap progress proses rehabilitasi pasien pasca terkena stroke. Hal ini terjadi karena tenaga medis dalam melakukan proses rehabilitasi masih menggunakan pencatatan progres secara konfensional.Metode yang digunakan dalam melakukan penelitian pengambilan data pasien pasca terkena stroke menggunakan sinyal Electroencephalograph (EEG) sebagai informasi pengambilan keputusan dalam melakukan tindakan lanjut dalam rehabilitasi medis menggunakan tahapan yaitu Persiapan, Studi Literature, Pemilihan Partisipan, Eksplorasi Alat, Pemilihan Gerakan, Pengambilan Data.Hasil akhir yang didapat sebagai output dalam penelitian pengambilan data pasien pasca terkena stroke menggunakan sinyal Electroencephalograph (EEG) sebagai informasi pengambilan keputusan dalam melakukan tindakan lanjut dalam rehabilitasi medis adalah berupa data yang telah terekan dalam bentuk raw data yang nantinya akan diolah lanjut untuk diproses sebagai media dalam penentuan penanganan rehabilitasi medis pasien stroke.Kata Kunci : Stroke, Rehabilitasi Medis, EEG, Pengukiran, Penilaian

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Identifying EEG Parameters to Monitor Stroke Rehabilitation using Individual Analysis

Cited by 14 publications

References 18 publications

G-Exos: A wearable gait exoskeleton for walk assistance

G-Exos: A wearable gait exoskeleton for walk assistance

EEG Time-Frequency Domain Analysis for Describing Healthy Subjects and Stroke Patients during Stroke Rehabilitation Motion Tasks

Pengambilan Data Pasien Stroke Menggunakan Sinyal EEG Sebagai Informasi Pengambilan Keputusan Melakukan Tindak Lanjut Rehabilitasi

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